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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Medical Physicsarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Medical Physics
Article . 2024 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
Medical Physics
Article . 2025
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Which failures do patient‐specific quality assurance systems need to catch?

Authors: Jennifer, O'Daniel; Victor, Hernandez; Catharine, Clark; Marco, Esposito; Joerg, Lehmann; Andrea, McNiven; Igor, Olaciregui-Ruiz; +1 Authors

Which failures do patient‐specific quality assurance systems need to catch?

Abstract

AbstractBackgroundThe Joint AAPM‐ESTRO TG‐360 is developing a quantitative framework to evaluate treatment verification systems used for patient‐specific quality assurance (PSQA). A subgroup was commissioned to determine which potential failure modes had the greatest risk to treatment quality and safety, and therefore should be evaluated as part of the PSQA verification.PurposeTo create an extensive database of potential radiotherapy failure modes that should be detected by PSQA and to determine their relative importance for maximizing treatment quality.MethodsThe subgroup consisted of eight physicists from seven countries, including representatives from three international quality assurance groups. We collected error reports from RO‐ILS, SAFRON, AAPM TG publications, and other literature, including international audits. We focused on the subset of failure modes that impact whether the planned dose matches the dose received by the patient. We performed a failure‐mode‐and‐effects analysis (FMEA), estimating the severity (S), occurrence (O), and detectability (D) of each failure mode. Detectability was scored assuming that PSQA was not done but other routine clinical QA was performed, which allowed us to see the importance of PSQA for detecting each specific failure mode. We analyzed the risk priority number (RPN = O*S*D), O*S, and severity rankings to determine the priority of each failure mode.ResultsWe collected 394 error reports, which we categorized into 33 failure modes that underwent FMEA. Five failure modes were in the top ranks for both RPN and O*S analysis: four involving treatment planning system (TPS) commissioning and one regarding patient model errors. The highest‐ranking RPN failure modes were: TPS algorithm limitations, TPS commissioning errors [multileaf collimator (MLC) modeling, output factor, percent‐depth‐dose/tissue‐maximum‐ratio (PDD/TMR), off‐axis factor], and patient weight variation. The highest O*S failure modes were similar, with the addition of external patient position variation and incorrect linear accelerator isocenter and cGy/monitor units calibration. RPN and O*S analyses prioritized failure modes that impacted multiple patients with high occurrence and detectability scores, while severity analysis gave higher priority to single‐patient modes with high severity scores. The highest‐ranking severity modes were MLC sequence deletion, collision, and TPS isocenter incorrect.ConclusionWe have developed a list of failure modes critical to be detected during PSQA and ranked them in order of importance. The top failure modes emphasize the importance of utilizing a variety of treatment verification systems for PSQA, from secondary dose calculation through in‐vivo dosimetry, in order to detect all possible errors. For failure modes in the top quartile, PSQA is critical. Without adequate PSQA, these errors may go undetected unless caught by an external audit. This analysis can be useful for optimizing PSQA workflows and for designing evaluations of treatment verification systems, and will be used by the Joint AAPM‐ESTRO TG‐360 to determine an appropriate validation strategy.

Keywords

Quality Assurance, Health Care, Radiotherapy Planning, Computer-Assisted, Humans, Radiotherapy Dosage, Precision Medicine

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Top 10%
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